Single cell RNA sequencing – a valuable tool for cancer immunotherapy: a mini review

Introduction

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An important role in tumour development and heterogeneity is played by the tumour microenvironment (TME), which is the environment around the tumour consisting of the extracellular matrix, fibroblasts, and immune cells, along with the blood vessels surrounding it [1, 2]. Through the mechanism of cellular communication, all the cell types within the tumour microenvironment interact with each other to promote tumour development [3]. Hence, the implementation of effective anticancer immunotherapeutic strategies requires an understanding of the crucial suppressive mechanisms of the TME [4, 5]. To assess and track the changes within the tumour, next-generation sequencing (NGS) methods can be used. NGS is a high-throughput sequencing technology used to analyse DNA and RNA samples at high speed and low cost. Single-cell RNA sequencing (scRNA-seq), an offshoot of the NGS technology, can provide information on a combination of genomic, transcriptomic, epigenomic, and immunological signatures at the level of a single cell [6]. In this mini-review, we aim to provide a brief overview of the pros and cons of scRNA technology and its use in cancer immunotherapy.

Overview of scRNA sequencing steps

Rare cell mutations that occur during the processes of tumour progression, invasion, metastasis, and activation of immune cells can be monitored by using the scRNA sequencing method [7, 8]. In order to detect the transcriptional activity of several genes, including mutated ones and those that act as immune checkpoints, scRNA-Seq can be combined with traditional techniques like flow cytometry to specifically screen for targets of immune modulation. In brief, the initial step in scRNA-seq is the isolation of single cells from a heterogeneous cell population, followed by the extraction of ribonucleic acid (RNA) from the single cell, barcoded reverse transcription, cDNA amplification, library preparation, and sequencing the library using NGS platforms like the C1 single cell autoprep system (Fluidigm) or chromium (10× genomics) [9], [10], [11], [12]. A comprehensive workflow for scRNA sequencing and analysis is provided in Figure 1.

Figure 1:

An overview of the SCRNA sequencing workflow and its application in immunotherapy. It was created by using BioRender.com.

Various techniques and platforms used in ScRNA sequencing

Isolation of the cells can be done by various methods, depending on the type of sample and its processing. The techniques used for single-cell isolation require skill and knowledge of cell morphology. There are two ways of cell isolation based on their physical properties and their cellular biological characteristics [13]. Here we have summarised various techniques used to isolate the single cell and their application in Table 1.

In the 10× Genomics platform, single cell suspensions are loaded onto gel beads containing the ingredients for reverse transcription along with cell and transcript-identifying barcodes [22]. This combination is unique as it can be used to distinguish the expression profile of each cell. Based on this combination, the depth of the sequencing step is calculated [13]. Approximately 20,000 cells with a multiplexing possibility of 12 samples per lane [23]. With this input, 4.8 billion reads can be achieved in depth. Either the Miseq or Hiseq platforms can be used to perform the scRNA sequencing [24]. Both gene expression (GEX) and T cell/B cell repertoire profiling can be combined for the same input of cells using the 10× platform.

Computational tools and pipelines for scRNA sequencing analysis

Choosing the right tool for scRNA sequencing data analysis is a major task. The output file generated by scRNA sequencing through the Illumina HiSeq/MiSeq platform is the Base Call (BCL) file. The first step in data analysis will be BCL to FastQ conversion using tools like “bcl2fastQ”, and CASAVA. The quality control for the FastQ file is evaluated by an open-source command-line tool called “FASTQC”. After FASTQC, the output files can be analysed for an application of our interest using different tools [25]. Quantifying gene expression, identifying cell populations, immune repertoire studies, single-cell resolution and spatial location are the areas of interest in the ScRNA data analysis. Spatial data can be analyzed by using Squidpy, Seurat, Giotto, and spatial experiment tools to view the resolution spatially [26]. Tools like Scirpy, Dandelion, and scRepertoire can help with adaptive immune receptor repertoire analysis using the ScRNA-seq dissociated data. Muon, Seurat, and CiteFuse are used to know the functions of surface proteins. Muon, ArchR, snapATAC, Signac, Scanpy, Seurat, and single-cell experiments are used to measure chromatin accessibility and intracellular transcriptome gene quantity [25, 27]. In parallel, 10× genomics developed an ScRNA analysis pipeline called the Cell Ranger, which provides comprehensive solutions for processing and analysing data generated using the 10× Genomics chromium platform [25, 28].

Applications of scRNA sequencing in immunotherapy

Effective immunotherapeutic strategies require suitable target antigens that facilitate optimal stimulation of T cells by antigen-presenting cells while circumventing immune checkpoints for enhancing or amplifying a cytotoxic effector cell response. Bulk genomics cannot fully capture the potential of large-scale sequencing data for target identification. This can be carried out effectively using scRNA technology [29]. Since heterogenous cell types are identified by their diverse gene expression patterns, including genomic instability, genetic alterations, and epigenetic modifications, this technique has resulted in the identification of new cellular subtypes whose roles in modulating both the TME and effector immune responses will become clearer in the near future [30]. T-cell receptor sequences hold the key to an effective immune response [31]. In the case of immune responses, a study by Schattgen et al. [32] identified a link between the length of the CDR3 region and the T cell differentiation status. Using the same scRNA technology, it was shown that the bulk of the response against Pneumocystis carinii, which causes PCP, was mounted by CD4⁺ T lymphocytes [33]. Such crucial links may also be found in the TME and aid in identifying newer targets for eliminating tumour cells.

In addition to target identification, scRNA technology may be useful in evaluating the mechanism of tumour cell response in the face of IMT agents when used alone or as adjuvants to chemotherapy [34]. At a single cell resolution, by detecting the transcriptional activity of immune checkpoint or multidrug resistance genes, the effect of these agents on specific cellular groups can be studied, making them invaluable tools in personalised medicine [9, 12]. Some of the novel applications of scRNA sequencing in immunotherapy are in studying the involvement of the immune system in cancer progression, the impact of drugs on antitumor immunity, and patient treatment outcome and effect [33], [34], [35]. scRNA-seq technology has been applied in the field of drug discovery and development to understand disease subtyping based on cell composition and altered cell states and will help us identify novel cellular and molecular targets for preclinical models [36]. scRNA-seq, along with the functional genomic CRISPR (clustered regulatory interspaced short palindromic repeat) screens, has enabled the exploration of gene function and genetic regulatory networks. This emerging technology for high-throughput investigations facilitates target prioritisation of highly active CRISPR libraries for single-cell screens [37]. Sc-CRISPR screens can assess specific CRISPR-driven gene knockdowns or editing and the resulting perturbed cellular phenotypes in a scalable and comprehensive manner [38].

Current challenges in using scRNA sequencing

scRNA technology is currently available as full-length transcript sequencing or 3′/5′ end sequencing alone. The full-length technologies that are currently available include SmartSeq2, MATQ-Seq, and SUPeR-Seq [39]. For the 3′ end sequencing, Drop Seq, Seq Well, chromium, and DroNc Seq are available. STRT Seq can perform 5′-end sequencing alone. However, currently, scRNA technology suffers from technical and analytical challenges [40, 41]. The technique requires a large amount of starting material and is plagued by low capture and high drop, along with abundant technical noise-related issues. Additionally, it is also limited by biological variations between each cell arising due to the stochastic nature of transcription [40, 41]. The overall quality of the scRNA sequencing data is dependent on the read mapping ratio. No difference exists between scRNA and bulk RNA sequencing data in read alignment. Most of the mapping, expression, and other data analysis tools that have become more streamlined for use across different platforms of bulk RNA sequencing can also be used for scRNA-seq data analysis [41]. However, data bias, such as coverage of the transcripts, capture effectiveness, and sequencing noise resulting from dead or damaged cells, plagues the analysis of scRNA data [42]. Due to such cell quality-related issues, there is the possibility of data misinterpretation. Hence, crucial QC analysis is required to filter this data. Determining the expression levels of rare or abundant events by scRNA-seq still remains a challenge. To some extent, this could be overcome by using the selective enrichment technique, which was developed based on CRISPR technology [43]. Hence, gold-standard technical and computational methods are needed to allow the reproducibility of clinical trials in onco-immunology between centres. Compared to bulk RNA-seq, the cost of performing scRNA-seq is less, but it may still need to be scaled down for use in routine clinical care [44].

Conclusions

Great progress in the past decade has been witnessed in scRNA-seq technology development and innovation. Advances in bioinformatics approaches and artificial intelligence to perform data analysis will facilitate biological and clinical researchers’ understanding of dynamics between distinct cell clusters within tumours, allowing immunotherapy to succeed in treating a variety of malignancies. Inevitably, such strategies may prevent tumour recurrence and increase patient survival.